code: def _fold_with_overlap(
      x: Tensor, timesteps: int, overlap: int
  ) -> Tensor:
      r"""Fold the tensor with overlap for quick batched inference.
      Overlap will be used for crossfading in xfade_and_unfold().
  
      x = [[h1, h2, ... hn]]
      Where each h is a vector of conditioning channels
      Eg: timesteps=2, overlap=1 with x.size(1)=10
      folded = [[h1, h2, h3, h4],
                [h4, h5, h6, h7],
                [h7, h8, h9, h10]]
  
      Args:
          x (tensor): Upsampled conditioning channels of size (1, timesteps, channel).
          timesteps (int): Timesteps for each index of batch.
          overlap (int): Timesteps for both xfade and rnn warmup.
  
      Return:
          folded (tensor): folded tensor of size (n_folds, timesteps + 2 * overlap, channel).
      """
  
      _, channels, total_len = x.size()
  
      # Calculate variables needed
      n_folds = (total_len - overlap) // (timesteps + overlap)
      extended_len = n_folds * (overlap + timesteps) + overlap
      remaining = total_len - extended_len
  
      # Pad if some time steps poking out
      if remaining != 0:
          n_folds += 1
          padding = timesteps + 2 * overlap - remaining
          x = torch.nn.functional.pad(x, (0, padding))
  
      folded = torch.zeros(
          (n_folds, channels, timesteps + 2 * overlap), device=x.device
      )
  
      # Get the values for the folded tensor
      for i in range(n_folds):
          start = i * (timesteps + overlap)
          end = start + timesteps + 2 * overlap
          folded[i] = x[0, :, start:end]
  
      return folded
  
file_path: audio/examples/pipeline_wavernn/wavernn_inference_wrapper.py
imports: import torch
  import torchaudio
  from processing import normalized_waveform_to_bits
  from torch import Tensor
  from torchaudio.models.wavernn import WaveRNN
label_desc: Use image node_id 1 for calling a global function with example usage: _fold_with_overlap(x, timesteps, overlap) and returns: folded